The present invention relates to evolution and learning in neural networks for use in various applications, such as, for example, pattern recognition techniques.
The structure and function of a biological network derives from both its evolutionary precursors and real-time learning. Genes specify (through development) coarse attributes of a neural system, which are then refined based on experience in an environment containing more information--and more unexpected information--than the genes alone can represent. Innate neural structure is essential for many high level problems such as scene analysis and language [Chomsky, 1957].
Although the Central Dogma of molecular genetics [Crick, 1970] implies that information learned cannot be directly transcribed to the genes, such information can appear in the genes through an indirect Darwinian process (see below). As such, learning can change the rate of evolution - the Baldwin effect [Baldwin, 1896]. Hinton and Nowian [1987] considered a closely related process in artificial neural networks, though they used stochastic search and not learning per se. Present here are analyses and simulations utilizing learning, and demonstration of selectivity for the effect.